Computational Privacy Group - Imperial College London

Today people leave digital breadcrumbs wherever they go and whatever they do online and offline. This data dramatically increases our capacity to understand and affect the behavior of individuals and collectives, has been key to recent advances in AI, but also raises fundamentally new privacy and fairness questions. The Computational Privacy Group aims to provide leadership, in the UK and beyond, in the safe, anonymous, and ethical use of large-scale behavioral datasets coming from the Internet of Things (IoT) devices, mobile phones, credit cards, browsers, etc.

Our projects have already demonstrated the limits of data anonymization (or de-identification) in effectively protecting the privacy of individuals in Big Data, the risk of inference in behavioral datasets coming from mobile phone, and developed solutions to allow individuals and companies to share data safely. While technical in nature, our work has had significant public policy implication for instance in reports of United Nations, FTC, and the European Commission as well as in briefs to the U.S. Supreme Court.

Research Areas

Identification Learning

We develop statistical and machine learning techniques to uniquely identify individuals in large-scale behavioral datasets. These techniques show the limits of pseudonymization and anonymization in protecting people's privacy.

Safe Data Sharing

We build privacy-preserving and conscientious techniques to collect and use data while respecting people's privacy. For instance, we're building with MIT the OPAL (Open Algorithms) platform to safely share location data and openPDS to give individuals control over their data.

Societal impact of AI

Modern privacy is not only about controlling the information but also the ability to control how this information is used e.g. for insurance pricing or ad-targeting. We study fairness in algorithmic-decision making and, more generally, the impact of AI on society.

News and Events

CPG at ICML 2024

Jul 22, 2024

Matthieu Meeus and Igor Shilov introduced their paper [Copyright Traps for Large Language Models](https://arxiv.org/pdf/2402.09363) at ICML 2024 in Vienna.

London Data Week session at Imperial

Jul 17, 2024

Nataša Krčo and Igor Shilov led a session about exploring the robustness of modern data privacy systems. Learn more [here](https://www.imperial.ac.uk/news/254914/imperials-computational-privacy-group-lead-session/).

CPG at Data For Policy 2024 Conference

Jul 11, 2024

Dr Yves-Alexandre de Montjoye hosted a session on using technology to detect illegal content and assessing the robustness of modern data privacy mechanisms.

More News

Tweets by CPG

Selected publications

Stevanoski, B., Cretu, A.-M., and de Montjoye Y. A. QueryCheetah: Fast Automated Discovery of Attribute Inference Attacks Against Query-Based Systems. ACM Conference on Computer and Communications Security (ACM CCS 2024) (2024).
Meeus, M., Jain, S., Rei, M. and de Montjoye Y. A. Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models. 33rd USENIX Security Symposium (USENIX Security 2024) (2024).
Selected Press: Le Monde
Meeus, M., Shilov, I., Faysse, M. and de Montjoye Y. A. Copyright Traps for Large Language Models. 41st International Conference on Machine Learning (ICML 2024) (2024).
Selected Press: MIT Technology Review, Nature News
Gadotti, A., Rocher, L., Houssiau, F., Cretu, A.-M., and de Montjoye Y. A. Anonymization: The imperfect science of using data while preserving privacy. Science Advances, 2024 (2024).
Cretu, A.-M., Jones, Daniel, de Montjoye Y. A, and Tople, Shruti. Investigating the Effect of Misalignment on Membership Privacy in the White-box Setting. In Proceedings on Privacy Enhancing Technologies 2024(3), 407–430. (2024).
Guan, V., Guépin, F., Cretu, A.-M., and de Montjoye Y. A. A Zero Auxiliary Knowledge Membership Inference Attack on Aggregate Location Data. Proceedings on Privacy Enhancing Technologies 2024(4) (PoPETS 2024) (2024).
Cretu, A.-M., Guépin, F., and de Montjoye Y. A. Correlation inference attacks against machine learning models. Science Advances, 2024 (2024).
Cretu, A.-M.*, Rusu, Miruna*, and de Montjoye Y. A. Re-pseudonymization Strategies for Smart Meter Data Are Not Robust to Deep Learning Profiling Attacks. In Proceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy (CODASPY ’24), June 19–21, 2024, Porto, Portugal. ACM, New York, NY, USA. (2024).
Guépin, F., Krčo, N., Meeus, M. and de Montjoye Y. A. Lost in the Averages: A New Specific Setup to Evaluate Membership Inference Attacks Against Machine Learning Models. ArXiv preprint (2024).
Meeus, M., Shilov, I., and de Montjoye Y. A. Mosaic Memory: Fuzzy Duplication in Copyright Traps for Large Language Models. ArXiv preprint (2024).
Guépin, F., Meeus, M., Cretu, A.-M., and de Montjoye Y. A. Synthetic is all you need: removing the auxiliary data assumption for membership inference attacks against synthetic data. 18th DPM International Workshop on Data Privacy Management, Sept 2023, The Hague (2023).
Meeus, M., Guépin, F., Cretu, A.-M., and de Montjoye Y. A. Achilles’ Heels: Vulnerable Record Identification in Synthetic Data Publishing. 28th European Symposium on Research in Computer Security (ESORICS), Sept 2023, The Hague (2023).
Meeus, M., Jain, S., and de Montjoye Y. A. Concerns about using a digital mask to safeguard patient privacy. Matters Arising in Nature Medicine, July 2023 (2023).
Jain, S., Cretu, A.-M., Cully, A. and de Montjoye Y. A. Deep perceptual hashing algorithms with hidden dual purpose: when client-side scanning does facial recognition. 2023 IEEE Symposium on Security and Privacy (SP) (2023).
Selected Press: Imperial College London News
Rocher, L., Tournier, A. J., & de Montjoye, Y. A. Adversarial competition and collusion in algorithmic markets. Nature Machine Intelligence (2023).
Selected Press: POLITICO Pro Fair Play, POLITICO Pro Morning Tech
Houssiau, F., Liénart, T., Hendrickx, J. and de Montjoye Y. A. Web privacy: a Formal Adversarial Model for Query Obfuscation. IEEE Transactions on Information Forensics and Security (2023).
Houssiau, F., Sapieżyński, P., Radaelli, L., Shmueli, E. and de Montjoye Y. A. Detrimental network effects in privacy: A graph-theoretic model for node-based intrusions. Patterns, 4(1) (2023).
Houssiau, F., Schellekens, V., Chatalic, A., Annamraju, S. K. and de Montjoye Y. A. M2M: A General Method to Perform Various Data Analysis Tasks from a Differentially Private Sketch. The 18th International Workshop on Security and Trust Management (2022).
Cretu, A.-M.*, Houssiau, F.*, Cully, A., and de Montjoye Y. A. QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systems. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (CCS '22). Association for Computing Machinery, New York, NY, USA, 623–637. (2022).
Selected Press: Imperial College London
Tournier, A. J., & de Montjoye, Y. A. Expanding the attack surface: Robust profiling attacks threaten the privacy of sparse behavioral data. Science Advances (2022).
Gadotti A., Houssiau F., Annamalai M.S.M.S., & de Montjoye, Y. A. Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice. 31st USENIX Security Symposium (2022).
Jain, S.*, Cretu, A.-M.*, and de Montjoye Y. A. Adversarial Detection Avoidance Attacks: Evaluating the robustness of perceptual hashing-based client-side scanning. 31st USENIX Security Symposium (2022).
Selected Press: Imperial College London News
Cretu, A.-M., Monti, F., Marrone, S., Dong, X., Bronstein, M. and de Montjoye Y. A. Interaction data are identifiable even across long periods of time. Nature Communications (13), 313 (2022).
Selected Press: Science News, RFI
Houssiau, F., Rocher, L. and de Montjoye Y. A. On the difficulty of achieving Differential Privacy in practice: user-level guarantees in aggregate location data. Nature Communications, 2022 (2022).
Gadotti A., Houssiau F., Rocher L., Livshits B., de Montjoye Y. A. When the signal is in the noise: Exploiting Diffix's Sticky Noise. 28th USENIX Security Symposium (2019).
Selected Press: TechCrunch, Wall Street Journal
Rocher, L., Hendrickx, J. M., & de Montjoye, Y. A. Estimating the success of re-identifications in incomplete datasets using generative models. Nature communications, 10 (1), 3069 (2019).
Selected Press: New York Times, Guardian, CNBC, The Telegraph, TechCrunch, Technology Review, New Scientist, Gizmodo, Scientific American, RT, Forbes, El Pais (ES), Sueddeutsche Zeitung (DE), Le Soir (FR), La Libre (FR), L'Echo (FR), De Morgen (NL)
de Montjoye Y. A., Radaelli L., Singh V. K., Pentland A., Unique in the shopping mall: On the reidentifiability of credit card metadata. Science 347 (6221), 536-539. DOI:10.1126/science.1256297 (2015).
Selected Press: New York Times, Wall Street Journal (1, 2), BBC, Harvard Business Review, Nature, Technology Review, Le Monde (FR), Die Zeit (DE), Die Spiegel (DE), El Pais (ES), The Hill, Les Echos (FR), Scientific American, New Scientist, Five Thirty Eight
de Montjoye Y. A., Shmueli E., Wang S., Pentland A., openPDS: Protecting the Privacy of Metadata through SafeAnswers. PLoS One, 10.1371 (2014).
Selected Press: BBC, New-York Times, Wall Street Journal, Technology Review, World Economic Forum, Real Time with Bill Maher (HBO), Le Monde (FR), MIT News, Wired (UK), New Scientist, Baratunde for Fast Company, GigaOM, Scientific American (1, 2)
de Montjoye Y.-A.*, Quoidbach J.*, Robic F.*, Pentland A., Predicting people personality using novel mobile phone-based metrics. International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction, Washington, USA (2013).
Selected Press: Le Monde (FR), RT, Boston Globe, Mediapart (FR), Radio Canada (FR)
de Montjoye Y.-A., Hidalgo C. A., Verleysen M., Blondel V. D., Unique in the Crowd: The privacy bounds of human mobility. Nature srep. 3, 1376; DOI:10.1038/srep01376 (2013).
Selected Press: BBC, CNN, Wall Street Journal, New-York Times ( 1, 2), Le Monde (FR) (1, 2), Guardian (UK), Technology Review, Nature, Wired (US, UK, JP), Bruce Schneier, Der Spiegel (DE), Die Welt (DE) (1, 2), Boing Boing, Fast Company, PopSci, GigaOM, TechDirt

The full list of our papers is available on Google Scholar.

Our Team

Yves-Alexandre de Montjoye

Group leader

Yves-Alexandre is an Associate Professor at Imperial College London. He received his PhD from MIT before joining Harvard IQSS for his postdoc. He currently is a Special Adviser on AI and Data Protection to EC Justice Commissioner Reynders and a Parliament-appointed expert to the Belgian Data Protection Agency (APD-GBA).

Euodia Dodd

PhD student

Euodia obtained her BSc in Computer Science from The University of Sheffield, and an MPhil in Advanced Computer Science from the University of Cambridge in 2022. She then spent two years at Goldman Sachs in the Applied AI team. Her interests include privacy attacks on ML systems, privacy-preserving ML, and interpretability.

Florent Guepin

PhD student

Florent received his BSc and MSc in theoretical computing from ENS Lyon. He also holds a BSc in pure maths from University Lyon 1 an engineering degree from Centrale Lyon. His research interests include privacy attacks against machine learning systems and generative systems such as GANs.

Yuhan Liu

Visiting PhD student

Yuhan obtained her BSc in computer science at Ximen University in China. While pursuing her Ph.D. degree at Renmin University of China since 2019, she worked as a research intern for a year at DAMO Academy, Alibaba Group. As a one-year visiting student, her research interests include differential privacy and privacy attacks against statistical data analysis and ML systems.

Nataša Krčo

PhD student

Nataša obtained her BSc degree in Computer Science at the University of Novi Sad in Serbia. After that, she obtained her MSc degree from EPFL. During her time there, she conducted research both in academic and industry settings, on responsible AI. Her research interests include explainable AI, algorithmic fairness, and privacy-preserving ML.

Yifeng Mao

PhD student

Originally from China, Yifeng obtained his BSc from Tsinghua University in Automation. After his master degree, he spent one year working as a full-time research assistant in the US. His research interests include privacy-preserving ML, synthetic data generation protecting personal anonymity, and attack methods against census datasets and ML models.

Matthieu Meeus

PhD student

Matthieu obtained his BSc from KU Leuven in Mechanical Engineering. Next, he spent four years in the US, pursuing two years of graduate study (Energy, Computer Science) and two years of working as data scientist at McKinsey & Company. His research interests include privacy attacks against (large) language models and ML systems.

Igor Shilov

PhD student

Igor obtained his undergraduate degree in Computer Science in 2013 and had been working as a Software Engineer since, most recently at Meta AI. His research interests include differential privacy and privacy attacks against ML systems.

Bozhidar Stevanoski

PhD student

Bozhidar has obtained his MSc in Data Science at the University of Ljubljana and his BSc in Computer Science at the Ss. Cyril and Methodius University in Skopje. His research interests include machine learning and privacy attacks against query-based systems.

Xiaoxue Yang

PhD student

Xiaoxue holds a BSc in Statistics/Economics from the University of Toronto and an MSc in Artificial Intelligence from Imperial College. Before her MSc, she worked as a technology consultant and later as a data scientist in Deloitte’s management consulting practice. Her research interests include privacy attacks against ML models and data releases such as synthetic data.

Zexi Yao

PhD student

Zexi is from Singapore, and obtained his BScs in Computer Science as well as Electrical and Computer Engineering from Carnegie Mellon University. He then spent 2 years as a research engineer at A*STAR in Singapore. His research interests include secure computing, distributed machine learning and privacy preserving techniques in machine learning.

Click here for more information on our team. →

Contact

Email:X@Y where X=demontjoye, Y=imperial.ac.uk.
Administrator (if urgent): Amandeep Bahia, +44 20 7594 8612

We are located at the Data Science Institute in the William Penney Laboratory. The best entry point is via Exhibition road, through the Business school (see map below). From there, just take the stairs towards the outdoor court. Enter the outdoor corridor after the court and the institute will be on your right (please press the Data Science intercom button for access).

Please address mails to:
Department of Computing
Imperial College London
180 Queens Gate
London SW7 2AZ

Somewhere